dynamics of cost efficiency in indian public sector banks

7/31/2019 Dynamics of Cost Efficiency in Indian Public Sector Banks

1/41

Dynamics of Cost Efficiency in Indian Public Sector Banks:

A Post-deregulation Experience

A paper submitted for presentation in the

Twelfth Annual Conference on Money and Finance in

The Indian Economy

11th and 12th March, 2010

By

Sunil Kumar

Reader in Economics,Punjab School of Economics,

Guru Nanak Dev University,

Amritsar-143005, Punjab, India.

Email: [email protected]

Rachita Gulati

Junior Research Fellow (JRF),

Punjab School of Economics,

Guru Nanak Dev University,

Amritsar-143005, Punjab, India.Email: [email protected]

1
mailto:[email protected]:[email protected]:[email protected]:[email protected]


2/41

Dynamics of Cost Efficiency in Indian Public Sector Banks:

A Post-deregulation Experience

Abstract:This paper analyses the trends of cost efficiency and its components across Indian public sector banks

(PSBs) during the post-deregulation period spanning from 1992/93 to 2007/08. The study also examines

the issue of convergence in cost, technical and allocative efficiencies levels of Indian PSBs. The empirical

results indicate that deregulation has had a positive impact on the cost efficiency levels of Indian public

sector banking industry over the period of study. Further, technical efficiency of Indian public sector

banking industry followed an upward trend, while allocative efficiency followed a path of deceleration.

We note that, in Indian public sector banking industry, the cost inefficiency is mainly driven by technical

inefficiency rather than allocative inefficiency. The convergence analysis reveals that the inefficient PSBs

are not only catching-up but also moving ahead than the efficient ones, i.e., the banks with low level of

cost efficiency at the beginning of the period are growing more rapidly than the highly cost efficient

banks. In sum, the study confirms a strong presence of - and - convergence in cost efficiency levelsof Indian public sector banking industry.

Keywords: Data envelopment analysis, Public sector banks, Cost efficiency, Technical

efficiency, Allocative efficiency, Convergence.

JEL Codes: G21, G15

2


3/41

1. Introduction

From the early 1970s through the late 1980s, the role of market forces in Indian banking

system was almost missing, and excess regulation in terms of high liquidity requirements andstate interventions in allocating credit and determining the prices of financial products has

resulted in serious financial repression. The main consequence of this financial repression was an

ascent in the volume of bad loans due to ineffective credit evaluation system and poorer riskassessment policies. Further, poor disclosure standards abetted corruption by window-dressing

the true picture of banks. The overstaffing and over-branching and undue interference by labour

unions resulted in huge operating losses. This led to a gradual decline in the profitability andefficiency of Indian banks, especially of public sector banks (PSBs). Infact, in late 1990s, Indian

banking system was on the verge of a crisis and lacking viability even in its basic function of

financial intermediation.

Realizing the presence of the signs of financial repression and to get an escape from anypotential crisis in the banking sector, Government of India (GOI) embarked on a comprehensive

banking reforms plan in 1992 with the objective to create a more diversified, profitable, efficient

and resilient banking system. The broad contour of this plan was sketched by the Committee on

the Financial System (Chairperson: M. Narasimham, 1991), while the definite shape to the planwas provided by the Committee on the Banking Sector Reforms (Chairperson: M. Narasimham,

1998. The main agenda of reforms process was to focus on key areas: i) restructuring of PSBs byimparting more autonomy in decision making, and by infusing fresh capital through

recapitalization and partial privatization; ii) creating contestable markets by removing entry

barriers for de novo domestic private and foreign banks; iii) improving the regulatory andsupervisory framework; and iv) strengthening the banking system through consolidation. To

meet this agenda, the policy makers heralded an episode of interest-rates deregulation,

standardized minimum capital requirements as per Basle norms, prudential norms relating to

income recognition, assets classification and provisioning for bad loans, and changes inregulatory and supervisory environment.

Given the broad sketch of banking reforms portrayed above, one may ask whether the

efficiency performance of PSBs since the launching of reforms in 1992 has improved or not. Inthis paper, we made an attempt in this direction. In particular, our endeavour here is to evaluate

the performance of PSBs in the post-reforms period by looking at the trends of cost efficiency

(CE) and convergence in its levels across banks. The paper has extended the existing literaturerelated to the efficiency of Indian banks in two directions. First, this study reports the bank-wise

analysis of trends of cost efficiency and its components, namely technical and allocative

efficiencies. Barring a few exceptions, most of existing studies on the efficiency of Indian banks

have reported the results for specific groups of banks (particularly defined by ownership andsize) rather than those of individual banks. However, we may get a misleading picture from a

group-specific analysis if one or a set of some out-performing bank(s) supersede the dismal

efficiency levels of the remaining banks of the group. The bank-wise results reported in thepresent study avoid the problem of dominance of one bank over others within the same group,

and would be more useful in designing micro-level policies in the banking industry. Second, to

best of our knowledge, this is perhaps the first empirical study that has analyzed the convergenceor divergence in the levels of cost efficiency and its distinct components.

Our analysis evolves in two steps. First, using the data of 27 PSBs over a period 16 years

(from 1992/93 to 2007/08), we calculate cost efficiency (CE), technical efficiency (TE) and

allocative efficiency (AE) scores for individual PSBs using the technique of data envelopment

3


4/41

analysis (DEA), a deterministic non-parametric frontier approach of efficiency measurement. In

recent years, many studies have appeared in academic journals that applied DEA to assess the

relative cost efficiency of banks (see, for example, Aly et al. 1990; Ferrier and Lovell, 1990;Maudos and Pastor, 2001; Isik and Hassan, 2002; Darrat et al., 2003; Elyasaini et al., 2003;

Burki and Dashti, 2003; Maudos and Pastor, 2003; Chen, 2004; Neal 2004; Chen et al. 2005;

Hassan 2005; Havrylchyk, 2005; Fiorentino et al., 2006; Matthews et al., 2006; Ariss et al.,2007; Rezvanian et al., 2007; Hassan and Sanchez, 2007; Barry et al., 2007; Rezvanian et al.

2007 ; Asimakopoulos et al.,2008; Isik and Darrat, 2008; Ariff and Can, 2008; Burki and Naizi,

2009; Shamsi et al., 2009; Brack and Jimborean, 2009; Awdeh and Moussawi, 2009; Roberta etal., 2009). Second, we use traditional cross-sectional regression approach for investigating the

presence of -convergence and -convergence in CE, TE and AE levels. In the contemporary

literature, similar approach has been used by Tomova (2005), Mamatzakis et al. (2007), and

Weill (2008), Brack and Jimborean (2009) to examine the convergence in bank efficiency levels

across European countries and by Daley and Matthews (2009) for testing the convergence inefficiency levels of Jamaican banks.

Our empirical investigation suggests that deregulation has had a positive impact on the

performance of Indian public sector banking industry in terms of cost efficiency over the entireperiod of 1992/93-2007/08. However, improvement in cost efficiency has been noticed to be

more pronounced in the years belong to second phase (1998/99-2007/08) relative to first phase

(1992/93-1997/98). Further, an average level of cost efficiency among Indian PSBs is to the tune

of 79.6%, indicating an average potential total production cost saving of 25.6% over 16 years, ifall banks had been full cost efficient. The disaggregate analysis reveals that cost inefficiency in

Indian public sector banking industry originates primarily due to technical inefficiency

(managerial problems in using the financial resources) rather than allocative inefficiency(regulatory environment in which PSBs are operating). Finally, the study reports the presence of

strong -convergence and -convergence in cost efficiency levels of Indian PSBs during the

deregulatory regime. Overall, Indian public sector banking industry not only experienced

significant efficiency gains during the post-reforms period but also witnessed strong

- and

-convergence in cost efficiency levels among PSBs.The paper is structured as follow. In the next section, we present the relevant literature

review of the studies aiming at studying the impact of liberalization and deregulation on the

efficiency and productivity of the banking system. Section 3 provides an overview of the process

of banking reforms in India. Section 4 presents the conceptual framework for measuring costefficiency and its components using DEA approach. Section 5 explains the methodological

framework for testing -convergence and -convergence using regression analysis.

Specification of bank inputs and outputs, and data are presented in Section 6. Section 7 discusses

the empirical findings and, finally, Section 8 concludes the paper.

2. Impact of Deregulation on Banking Efficiency and Productivity: Literature Review

2.1 International experienceOne of the most studied issues in banking efficiency literature during the last years has

been the impact of liberalization and deregulation on the efficiency and productivity of the

banking system. In theory, financial liberalization is expected to improve bank efficiency (Bergerand Humphrey, 1997). The elimination of government control and intervention aims at restoring

and strengthening the price mechanism, as well as improving the conditions for market

competition (Hermes and Lensink, 2008). This stimulates the efficiency of banks in resourceutilization process. Competitive pressure stimulates banks to become more efficient by reducing

4


5/41

overhead costs, improving on overall bank management, improving risk management and

offering new financial instruments and services (Denizer et al., 2000). Since 1990s, there is a

flurry of studies on the effect of deregulation on efficiency and productivity of banks.Nevertheless empirical studies investigating the relationship between financial deregulation and

efficiency of banks provide mixed results.

In context of Norwegian banking industry, Berg et al. (1992) reported a productivityregress at the average bank prior to the deregulation, but rapid growth when deregulation took

place. Zaim (1995) concluded that the post-1980 financial liberalization policies succeeded in

enhancing both technical and allocative efficiency of Turkish banks. Leightner and Lovell (1998)observed that from the perspective of commercial bank objective, financial liberalization had a

significant and positive impact on total factor productivity growth of Thai banks. Lozano-Vivas

(1998) painted a more positive picture regarding the effects of deregulation on the Spanish

banking industry in terms of cost efficiency. Rebelo and Mendes (2000) noted an improvementin the efficiency and productivity of Portugese banks during the deregulation period. The

findings of the study of Ali and Gstach (2000) revealed that deregulation in Austrian banking

industry spurred the competition which in turn brought an improvement in efficiency.

Kumbhakar et al. (2001), and Kumbhakar and Lozano-Vivas (2005) concluded that deregulationcontributed positively to TFP growth for Spanish banks. Maghyereh (2004) noted that financial

liberalization program of early 1990s was successful in bringing an observable increase in theefficiency of Jordian banks. Chen et al. (2005) found that financial deregulation of 1995 was

successful to improve cost efficiency levels of Chinese banks including both technical and

allocative efficiency. Hua and Randhawa (2006) observed that deregulation in banking sectors ofHong Kong and Singapore has yielded the desired results in terms of efficiency improvement.

Retizis(2006) found that productivity growth in Greek Banking industry is clearly higher after

deregulation. Fethi et al. (2009) noted that liberalization and privatization policies adopted by the

Egyptian government in 1991 and late 1995 respectively have managed to improve the efficiencyof the banking sector overall. Hermes and Nhung (2008) observed a positive impact of financial

liberalization programme on efficiency of banking sectors of ten Latin American and Asian

countries. Jiang et al. (2009) reported improved efficiency levels for Chinese banks during post-reforms period. Burki and Ahmad (2009) found that X-inefficiency of Pakistan banks decreased

over the reform period.

In contrast to aforementioned studies that painted a rosy picture about the impact of

deregulation on the efficiency and productivity of banking system, the following studies present

the instances where a negative or insignificant effect has been observed. Humphrey (1993)

observed that deregulation was found to have negative effect on US bank productivity.Grabowski et al. (1994) concluded that the empirical results relating with US banks do not

appear to support the hypothesis that deregulation had a favorable effect on the economic

efficiency of banking firms. Grifell-Tatj and Lovell (1996) observed a negative productivity

change in Spanish saving banking industry. Humphrey and Pulley (1997) also found that theproductivity of US banks has fallen because deregulation of interest rates in the early 1980s

raised bank funding costs and lowered profits. . Denizer et al. (2000, 2007) painted a verygloomy picture and concluded that liberalization programs were followed by a decline in

efficiency among Turkish banks. Christopoulos and Tsionas (2001) reported that deregulation

has brought no significant improvement in the technical and allocative inefficiencies of Greek

banks. Hao et al. (2001) noticed that in Korea, the financial deregulation of 1991 was found tohave had little or no significant effect on the level of bank efficiency. Cook (2001) concluded

5


6/41

that in Tunisia, deregulation has been less successful in closing the efficiency gap between

public, private, and foreign banks. Dogan and Fausten (2003) revealed deterioration in the

Malaysian commercial banks productivity in the post-liberalisation era. Kamberoglou et al.(2004) noted that scale economies have declined throughout the post-deregulation period. In

context of Chinese banking industry, Kumbhakar and Wang (2007) found no evidence to support

the view that deregulation improved the efficiency of banks significantly. Moffat et al. (2008)noted a loss or little productivity gain in Botswanas banks during the post-reform years. Naceur

et al. (2009) observed that the effect of deregulatory and liberalization initiatives on bank

efficiency and performance in Egypt, Jordan, Morocco, and Tunisia has been limited.

2.2 Indian experience

The literature concerning to bank efficiency in India shows that good number of studies

has assessed the impact of transition from regulation to competition on the efficiency andproductivity of banks. The most of literature on the effect of deregulation and liberalization on

Indian banking industry portraits a positive impact of deregulatory policies on the efficiency and

productivity of Indian banks. Followings are the key findings of the prominent studies in Indiancontext. The study of Bhattacharya et al. (1997a) divulged that deregulation has led to the

improvement in the overall performance of Indian commercial banks. Bhattacharyya et al.

(1997b) also reported a positive impact of deregulation on the TFP growth of Indian publicsector banks. Ram Mohan and Ray (2004) found an improvement in the revenue efficiency of

Indian banks. Also, they noticed convergence in performance between public and private sector

banks in the post-reform era. Shanmugam and Das (2004) observed that during the deregulationperiod, the Indian banking industry showed a progress in terms of efficiency of raising non-

interest income, investments and credits. Ataullah et al. (2004) reported that overall technical

efficiency of the banking industry of India and Pakistan improved following the financial

liberalization. Das et al. (2005) the efficiency of Indian banks, in general, and of bigger banks, inparticular, has improved during the post-reform period. The findings of the study of Mahesh and

Rajeev (2006) are completely similar to that of Shanmugam and Das (2004). Sensarma (2006)

noted that deregulation in Indian banking industry (especially public sector banks) achieved theaim of reduction in intermediation costs and improving TFP. On comparing the effect of

deregulation on the productivity growth of banks in Indian sub-continent(including India,

Pakistan and Bangladesh), Jaffry et al.(2007) concluded that technical efficiency both increasesand converges across the Indian sub-continent in response to reform. Zhao et al. (2007) noted

that, after an initial adjustment phase, the Indian banking industry experienced sustained

productivity growth, driven mainly by technological progress. Sahoo et al. (2007), and Sahoo

and Tone (2009) observed that the government reform process instituted in the banking industryhas had a favourable effect on the performance of the Indian banking industry. Mahesh and

Bhide (2008) found that deregulation has a significant positive impact on the cost and profit

efficiencies of commercial banks. Das and Ghosh (2009) concluded that the liberalization of the

banking sector in India has generally produced positive results in terms of improving the costand profit efficiencies of banks.

In Indian context too, the mixed results have also been noticed. For example, Kumbhakarand Sarkar (2003) concluded that a significant TFP growth has not been observed in Indian

banking sector during the deregulatory regime. Further, public sector banks have not responded

well to the deregulatory measures. Galagedera and Edirisuriya (2005) observed that deregulation

has brought no significant growth in the productivity of Indian banks. Sensarma (2005) pointedout that profit efficiency of Indian banks has shown a declining trend during the period of

6


7/41

deregulation. Das and Ghosh (2006) found that the period after liberalization did not witness any

significant increase in number of efficient banks and some banks have high degree of

inefficiency during the period of liberalization.

3. Banking Sector Reforms in India

From the late 1960s through the early 1990s, Indian banks, especially the PSBs

essentially served as agents of the government in channelizing the investment resources toselected sectors under the countrys economic development policy. The development strategy

was designed to accelerate Indias transition from an agrarian economy to a self-reliant

industrialized state. The direct involvement of the state in economic development processresulted in the heavily regulated markets with distorted price mechanism. The financial market

was not an exception. Indian banking industry was heavily controlled by the government, and

characterized by extensive financial repression. The dominance of state-owned banks was visible

and perceptible since their share in industrys total assets was over 85 percent. The prime goal ofthe banking system was to serve better the needs of the development of the economy in

conformity with the national policy and objectives (Mohan and Prasad, 2005). In this period,

PSBs expanded through a network of more than 65,000 branches and their operations were

guided primarily by the social and political considerations rather than by the considerations ofprofitability.

Up until the launching of banking reforms in 1992, Government of India used thebanking system as an instrument of public finance (Hanson and Kathuria, 1999). Substantial and

increasing volumes of credit were channeled to the government at below-market rates through

high and increasing cash reserve requirements (CRR) and statutory liquidity requirements (SLR)in order to fund a large and increasing government deficit at relatively low cost (Sen and Vaidya,

1997)1. The commercial banks, especially, PSBs were obliged to allocate a substantial part of

their total loan portfolio to priority sectors (such as agriculture and small-scale industries) at a

rate that was below the market rate of interest. Furthermore, interest rates on both deposits andadvances were completely administered by the RBI. There was virtually no autonomy to the

banks even in taking decision to open new bank branches. The government also tightly regulated

the licensing of market entry of new domestic and foreign banks. As a result PSBs dominated themarket. Indian PSBs stumbled downhill throughout the period 1980-1992 since non-performing

assets had continued to pile up whilst standard assets were doing little to return any significant

profits for the banks.Besides this, there were many weaknesses in the organizational structure ofbanks - lack of delegation, weak internal controls, and nontransparent accounting standards

(Mohan and Prasad, 2005). In sum, all the signs of financial repression such as excessively high-

reserve requirements, credit controls, interest rate controls, strict entry barriers, operational

restrictions, pre-dominance of state-owned banks, etc, were present in the Indian banking system.The extensively repressed financial environment led to inefficiency in credit allocation

and eroded the profitability of banks. The inefficiency in the deployment of credit and

deteriorating bank profitability also went hand in hand with inadequate capitalization andinsufficient provisions for bad debts by the banks. Jagirdar (1996) observed that the average

return on assets (ROA) in the second half of the 1980s was only about 0.15 percent which was

abysmally low by all standards. Further, in 1992/93, non-performing assets (NPAs) of 27 PSBsamounted to 24 percent of total credit, only 15 PSBs achieved a net profit, and half of the PSBs

faced negative net worth (Shirai, 2002). On commenting the state of Indian banking industry in

1By 1991, the pre-emptions under the cash reserve ratio and the statutory liquidity ratio, on an incremental basis,

had reached 63.5 percent of net demand and time liabilities

7


8/41

the pre-reform period, Sarkar (2004) remarked that the rates of return were low by international

standards, the capital base had eroded, non-performing assets were on the rise, and customer

service was below expectation. Further, the lack of proper disclosure norms led to manyproblems being kept under cover. Poor internal controls raised serious doubts about the integrity

of the system itself (Reddy, 1998). In such an environment, PSBs had little motivation to

improve their performance by reducing operating costs and improving the efficient allocation ofloans.To get rid of distressed banking situation, the Government of India embarked on a

strategy of reform measures in the financial sector, in general and banking sector, in particular.Note that the banking reforms in India had two distinct phases. The first phase of reforms

introduced consequent to the release of the Report of the Committee on the Financial System

(Chairperson: M. Narasimham), 1992.The focus of this phase of the reforms was economic

deregulation targeting at relaxing credit and interest rates controls, and removing restrictions onthe market entry and diversification. The second phase of reforms, introduced subsequent to the

recommendations of the Committee on Banking Sector Reforms (Chairperson: M. Narasimham),

1998. This phase targeted on enhancing prudential regulations, and improving the standards of

disclosure and levels of transparency so as to minimize the risks banks assume and to ensure thesafety and soundness of both individual banks and the Indian banking system as a whole. On the

whole, the key objective of the banking reforms was to transform the operating environment ofthe banking industry from a highly regulated system to a more market-oriented one, with a view

to increase competitiveness and efficiency (Sarkar, 2004).

Although the broad contours of reform measures in the banking sector have been

provided by the aforementioned committees but a large number of committees/working groups

have been constituted for addressing the specific issues in the banking sector. For example,

Janakiraman Committee (1992) investigated irregularities in fund management in commercialbanks and financial institutions. Padmanabhan Committee (1996) focused on the on-site

supervision of banks, and recommended the implementation of CAMELS rating methodology

for on-site supervision of the banks. Khan Committee (1997) suggested measures for bringingabout harmonization in the lending and working capital finance by banks and Development

Financial Institutions (DFIs). Verma Committee (1999) concentrated on restructuring of weak

PSBs. The committee identified three weak banks, viz. Indian Bank, United Commercial Bankand United Bank of India, and suggested introducing Voluntary Retirement Fund enabling bank

to reduce excess manpower. Vasudevan Committee (1999) recommended the strategy of up

gradation of the existing technology in the banking sector. Mittal Committee (2000) made vital

recommendations on the regulatory and supervisory frameworks for internet banking in India.Mohan Committee (2009) which is popularly known as Committee on Financial Sector

Assessment has suggested significant measures to improve the stability and resilience of Indian

financial system.

In post-1992 period, the reform measures have been taken in six directions for improving

the efficiency and profitability of Indian banks, (see Reddy, 2002; Rangarajan, 2007; Ahluwalia,2002; Shirai, 2002, for details). First, for making available a greater quantum of resources for

commercial purposes, the statutory pre-emptions have gradually been lowered2. Second, the

2The combined pre-emption under CRR and SLR, amounting to 63.5 percent of net demand and time liabilities in

1991 (of which CRR was 25 percent) has since been reduced and presently, the combined ratio stands below 35

percent (of which, the SLR is at its statutory minimum at 25 percent).

8


9/41

structure of administered interest rates has been almost totally dismantled in a phased manner3.

Third, the burden of directed sector lending has been gradually reduced by (a) expanding the

definition ofpriority sector lending, and (b) liberalizing lending rates on advances in excess ofRs. 0.2 million. Fourth, entry regulations for domestic and foreign banks have been relaxed to

infuse competition in the banking sector4. Fifth, the policy makers introduced improved

prudential norms related to capital adequacy5

, asset classification6

and income recognition in linewith international norms, as well as increased disclosure level7. Sixth, towards strengthening

PSBs, GOI recapitalized public sector banks to avert any financial crisis and to build up their

capital base for meeting minimum capital adequacy norms8.

4. Methodological framework

An analytical framework to measure cost efficiency9 of a firm dates back to the seminal

work of Farrell (1957). Measuring cost efficiency requires the specification of an objectivefunction and information on market prices of inputs. If the objective of the production unit is that

of cost minimization, then a measure of cost efficiency is provided by the ratio of minimum cost

to observed cost (Lovell, 1993). In Farrells framework, the cost efficiency (CE) is composed of

two distinct and separable components: technical efficiency (TE) - the ability of a firm to produceexisting level of output with the minimum inputs (input-oriented), or to produce maximal output

from a given set of inputs (output-oriented); and allocative efficiency (AE) - the ability of a firm

to use the inputs in optimal proportions, given their respective prices. Allocative efficiencyrelates to prices, while technical efficiency relates to quantities (Barros and Mascarennas, 2005).

Thus, cost inefficiency incorporates both allocative inefficiency from failing to react optimally to

relative prices of inputs and technical inefficiency from employing too much of the inputs toproduce a certain output bundle (Gjirja, 2004). It is noteworthy here that technical inefficiency is

caused and correctable by management, and allocative inefficiency is caused by regulation and

may not be controlled by the management (Hassan, 2005) An illustration of these efficiencymeasures as well as the way they are computed is given in Figure 1.

3Except saving deposit account, non-resident Indian (NRI) deposits, small loans up to Rs. 0.2 million and export

credit, the interest rates are fully deregulated.4 In 1993, the RBI issued guidelines concerning the establishment of new private sector banks. Nine new private

banks have entered the market since then. In addition, over twenty foreign banks have started their operations since1994.5 India adopted the Basel Accord Capital Standards in April 1992. An eight percent capital adequacy ratio was

introduced in phases between 1993-1996, according to banks ownership and scope of their operations. Following the

recommendations of Narasimham Committee II, the regulatory minimum capital adequacy ratio was later raised toten percent in the phased manner.6 The time for classification of assets as non-performing has been tightened over the years, with a view to move

towards the international best practice norm of 90 days by end 2004.7 From 2000-2001, the PSBs are required to attach the balance sheet of their subsidiaries to their balance sheets.8The GOI has injected about 0.1 percent of GDP annually into weak public sector banks (Hanson 2005, Rangarajan

2007). During the period 1992/93 to 2001/02, GOI contributed some Rs. 177 billion, about 1.9 percent of the

1995/96 GDP, to nationalized banks (Mohan and Prasad 2005).9In banking efficiency literature, the term cost efficiency is being used interchangeably with economic efficiency, X-

efficiency and overall efficiency.

9


10/41

In Figure 1,it is assumed that the firm uses two inputs, 1X and 2X , to produce output Y .

The firms production frontier 1 2( , )Y f X X = is characterized by constant returns-to-scale, so

that 1 21 ( , );f X Y X Y= and the frontier is depicted by the efficient unit isoquant o oY Y . A firm is

technically efficient if it is operating on o oY Y . However, technical inefficiency relates to an

individual firms failure to produce ono o

Y Y . Hence, firmPin the figure is technically inefficient.

Thus, for firm P, the technical inefficiency can be represented by the distance QP. A Farrells

measure ofTEis the ratio of the minimum possible inputs of the firm (i.e., inputs usage on the

frontier, given its observed output level) to the firms observed inputs. Accordingly, the level of

TE for firm P is defined by the ratio OQ OP. It measures the proportion of inputs actually

necessary to produce output. Allocative inefficiencies result from choosing the wrong inputcombinations given input prices. Now suppose that 'CC represents the ratio of input prices so

that cost minimization point is 'Q . Since the cost at point R is same as the cost at 'Q , we

measure the AEof the firm as OR OQ , where the distance RQ is the reduction in production

costs which could occur if production occurs at 'Q . Finally, the cost efficiency of the firm is

defined as OR OP, which can be considered a composite measure efficiency that includes both

technical and allocative efficiencies. In fact, the relationship between CE, TE, and AE is

expressed as:

( ) ( ) ( )

CE TE AE

OR OP OQ OP OR OQ

=

=

10


11/41

Most empirical analyses pertaining to the measurement of cost efficiency in banking

industry applied either parametric or non-parametric methods. These approaches use different

techniques to envelop the observed data and make different accommodations for random noiseand for the flexibility in the structure of the production technology (Lovell, 1993). In parametric

approaches, a specific functional form of the production function like Cobb-Douglas and

transcendental logarithmic (translog), etc. is required to specify a priori. The efficiency is thenassessed in relation to this function with constant parameters and will be different depending on

the chosen functional form. The most commonly used parametric methods are the Stochastic

Frontier Approach (SFA), the Thick Frontier Approach (TFA), and the Distribution FreeApproach (DFA). In contrast, non-parametric approaches do not specify a functional form, and

involve solving linear program, in which an objective function envelops the observed data; then

efficiency scores are derived by measuring how far an observation is positioned from the

envelope or frontier (Delis et al., 2009). The most widely used non-parametric approaches areData Envelopment Analysis (DEA) and Free Disposal Hull (FDH). However, no consensus has

been reached in the literature about the appropriate and preferred estimation methodology (Iqbal

and Molyneux, 2005; Staikouras et al., 2008).

For getting a convenient decomposition of cost efficiency, this paper uses data

envelopment analysis (DEA) to estimate empirically the cost, technical and allocative efficiencyscores for individual public sector banks. The computational procedure used to implement the

DEA approach to the measurement of cost efficiency and its components is of three steps. The

first step is to obtain the measure ofTEas introduced by Charnes et al. (1978). ConsiderKbanks

each of which uses N inputs to produce M outputs. For each bank 1,.....,i K= denote input

quantities by nix , 1,.....,n N= , and output quantities by miy , 1,.....,m M= , with 0nix > and

0miy > , i.e., each DMU has at least one strictly positive input and one strictly positive output.

Denote by Ya MKmatrix of outputs with bank is output in column i. Similarly,Xis aNK

matrix of inputs. A measureCRSi iTE = of technical efficiency can be calculated as a solution to

,min

subject to

,

,

free,

0

i i

CRSi i

i i

i i i

i

i

TE

Y y

X x

=

(1)

By solving linear programming problem (1), we identify a linear combination, described

by theK1 vector of i

of weights, of all banks in the sample which produces at least the outputquantities iy of banki and uses no more than a share (0,1]i of its inputs ix . Banks with a non-

zero weight in i are called reference banks for the bank i. For 1i = , a bank is called technically

efficient; i then has a value of 1 at element i as the only non-zero element. The way the problem

was set up ensures that 0i > and 1i . By minimizing i , we maximize the proportionate

reduction of bankis inputs.

11


12/41

The second step is to calculate cost efficiency by solving the following linear program

(see Fare and Grosskopf, 1985; Ferrieret al., 1993; for details).

'

,min

subject to

,

,

free,

0

i ii i

i i

i i

i

i

xw x

Y y

X x

x

(2)

where iw denotes the vector of input prices for banki. This yields a cost-minimizing input vector

ix and a linear combination i of all banks which produces at least bankis outputs iy and uses no

more than its ideal input vectorCRSix under a CRS technology. From the solution to model (2),

we get minimum costs as' CRSi iw x . Comparing minimum costs to observed costs

'i iw x of banki

gives cost efficiency as'

'

CRSCRS i ii

i i

w xCE

w x=

The third step involves the calculation of allocative efficiency component residually as

the ratio of the measure of cost efficiency to the Farrell input-oriented input-oriented measure oftechnical efficiency. Thus, the measure of allocative efficiency is obtained as:

CRSCRS ii CRS

i

CEAE

TE=

This relationship facilitates the decomposition of cost efficiency asCRS CRS CRS i i iCE TE AE = . Note that the measures of cost, technical and allocative efficiencies

range between 0 and 1. Corresponding to these efficiency measures, the measures of inefficiency

can be obtained as1 1 1( 1), ( 1), and ( 1)

i i iCE TE AE , respectively (See Isik and Hassan, 2002;

Welzel and Lang, 1997).

4. Data and measurement of input and output variables

In computing the efficiency scores, the most challenging task that an analyst always

encounters is to select the relevant inputs and outputs for modeling banks behaviour. It is worth

noting here that there is no consensus on what constitute the inputs and outputs of a bank (Casu

and Girardone 2002, Sathye 2003). In the literature on banking efficiency, there are mainly twoapproaches for selecting the inputs and outputs for a bank: i) the production approach, also

called theservice provision orvalue added approach; and ii) the intermediation approach, also

called the asset approach (Humphrey 1985, Hjalmarsson et al. 2000). Both these approachesapply the traditional microeconomic theory of the firm to banking and differ only in the

specification of banking activities. The production approach as pioneered by Benston (1965)

treats banks as the providers of services to customers. The output under this approach representsthe services provided to the customers and is best measured by the number and type of

12


13/41

transactions, documents processed or specialized services provided over a given time period.

However, in case of non-availability of detailed transaction flow data, they are substituted by the

data on the number of deposits and loan accounts, as a surrogate for the level of servicesprovided. In this approach, input includes physical variables (like labour, material, space or

information systems) or their associated cost. This approach focuses only on operating cost and

completely ignores interest expenses.The intermediation approach as proposed by Sealey and Lindley (1977) treats banks as

financial intermediaries channeling funds between depositors and creditors. In this approach,

banks produce intermediation services through the collection of deposits and other liabilities and

their application in interest-earning assets, such as loans, securities, and other investments. Thisapproach is distinguished from production approach by adding deposits to inputs, with

consideration of both operating cost and interest cost. Berger and Humphrey (1997) pointed out

that neither of these two approaches is perfect because they cannot fully capture the dual role ofbanks as providers of transactions/document processing services and being financial

intermediaries. Nevertheless, they suggested that the intermediation approach is best suited for

analyzing bank level efficiency, whereas the production approach is well suited for measuring

branch level efficiency. This is because, at the bank level, management will aim to reduce totalcosts and not just non-interest expenses, while at the branch level a large number of customer

services processing take place and bank funding and investment decisions are mostly not under

the control of branches. Also, in practice, the availability of flow data required by the productionapproach is usually exceptional rather than in common.

Elyasiani and Mehdian (1990) gave three advantages of the intermediation approach overother approaches. They argue that (a) it is more inclusive of the total banking cost as it does not

exclude interest expense on deposits and other liabilities; (b) it appropriately categorizes the

deposits as inputs; and (c) it has an edge over other definitions for data quality considerations.Therefore, as in majority of the empirical literature, we adopted a modified version of

intermediation approach as opposed to the production approach for selecting input and outputvariables for computing CE, TE and AE scores for individual PSBs. Table 1 provides the

description of the variables used in measurement of cost efficiency and its components.

13


14/41

Table 1: Definition of variables used in efficiency measurement

Variable Description in the balance sheet Unit of measurement

Total cost (TC) Rent, taxes and lighting + Printing and stationary +

Depreciation on banks property + Repairs and

maintenance + Insurance + Payment to and provisions foremployees + Interest paid on deposits + Interest paid on

borrowings from RBI and other agencies

Rupee lacs

Output variables

1) Net-interest income ( 1y )Interest earned - Interest expended Rupee lacs

2) Non-interest income ( 2y )Other income Rupee lacs

Input variables

1) Physical Capital ( 1x )Fixed assets Rupee lacs

2) Labour ( 2x )Staff Number

3) Loanable Funds ( 3x )Deposits + Borrowings Rupee lacs

Input prices1) Price of physical capital ( 1w )

(Rent, taxes and lighting + Printing and stationary + Depreciation on banks property

+ Repairs and maintenance + Insurance) / Fixed assets

2) Price of labour ( 2w )(Payment to and provisions for employees) / staff

3) Price of loanable funds ( 3w )(Interest paid on deposits + Interest paid on borrowings from RBI and other agencies)

/ Loanable funds

Note: 10 lacs=1 million

Source: Authors elaboration

The output vector contains two output variables: i) net-interest income, and ii) non-

interest income. The variable net-interest income connotes net income received by the banks

from their traditional activities like advancing of loans and investments in government and otherapproved securities. The output variable non-interest income accounts for income from off-

balance sheet items such as commission, exchange and brokerage, etc. The inclusion of non-

interest income enables us to capture the recent changes in the production of services as Indianbanks are increasingly engaging in non-traditional banking activities. As pointed out by Siems

and Clark (1997), the failure to incorporate these types of activities may seriously understate

bank output and this is likely to have statistical and economic effects on estimated efficiency.

Some notable banking efficiency analyses that include non-interest income as an output

variable are Isik and Hassan (2002a, 2002b), Drake and Hall (2003), Sufian (2006), Sufian and

Majid (2007), Hahn (2007) among others. Further, majority of the studies on efficiency of Indianbanks have also included non-interest income in the chosen output vector. It is worth noting

here that our choice of output variables is consistent with the managerial objectives that are

being pursued by the Indian banks. In the post-reforms years, intense competition in the Indianbanking sector has forced the banks to reduce all the input costs to the minimum and to earn

maximum revenue with less of less inputs. In this context, Ram Mohan and Ray (2004) rightly

remarked that in the post-liberalization period, Indian banks are putting all their efforts in the

business of maximizing incomes from all possible sources.

14


15/41

The input variables used for computing cost efficiency are i) physical capital, ii) labour,

and iii) loanable funds, which are proxied by fixed assets, staff, and deposits plus borrowings,

respectively. Correspondingly, the prices of these inputs are worked out as per unit price ofphysical capital, per employee wage bill, and cost of loanable funds. The details on the

definitions of these variables are given in the above table. The required data on the variables

used for computing various efficiency measures have been culled out from the various issues ofStatistical Tables Relating to Banks in India, an annual publication of Reserve Bank of India

and Performance Highlights of Public Sector Banks, an annual publication of Indian Banks

Association. In the terminal years of the study, 28 PSBs were operating in India and data on theIDBI Ltd. (a new public sector bank) were available only after 2004/05. Therefore, we excluded

this bank from the sample and confined the study to 27 PSBs that were operating in the Indian

banking sector during the period spanning from 1992/93 to 2007/08. Following Barman (2007)

and Roland (2008), we bifurcated the entire study period into distinct sub-periods: i) first phaseof banking reforms (1992/93 to 1998/99), and ii) second phase of banking reforms (1999/2000 to

2007/08). To compute CE scores, the analysis has been carried out with real values of the

variables (except labour) which have been obtained by deflating the nominal values by the

implicit price deflator of gross domestic product at factor cost (base 1993-94=100). FollowingDenizer et al. (2007), we normalized all the input and output variables by dividing them by

number of branches of individual banks for the given year. The main purpose of using thisnormalization procedure is that it reduces the effects of random noise due to measurement error

in the inputs and outputs.

5. Empirical results

This section delineates the trends of cost efficiency and its sources, namely, technical and

allocative efficiencies, in Indian public sector banking industry at an aggregate and bank levelsduring the post-deregulation period. Also, the results concerning convergence in efficiency levels

across PSBs are presented here.

5.1 Trends in cost (in)efficiencyat aggregate levelPanel A of Table 1 provides year-wise mean estimates of cost, technical and allocative

efficiencies for Indian public sector banking industry and its distinct sub-groups. The results

show that there are noticeable variations across years in cost efficiency levels, and there appearsto be an upward trend in the cost efficiency of Indian public sector banking industry. The cost

efficiency increased consistently from 71% in 1992/93 to 80.6% in 1997/98, and then declined

gently and reached to the level of 76.3% in 2001/02. Subsequently, a precipitous uplift in cost

efficiency has been noticed which ceased at the level of 86.7% in 2006/07. However, costefficiency turned down and attained a level of 81.6% in the terminal year. We further note that

the average level of cost efficiency (inefficiency) in Indian public sector banking industry is

79.6% (25.6%). The 79.6% efficiency figure means that the average bank in the sample could

have produced the same level of outputs using only 79.6% of the resources actually employed, ifit were producing on the frontier rather than at its current location . On the other hand, the 25.6%

inefficiency figure implies that in each year of the study period, the average bank needed 25.6 %more resources and, thus, incurred more cost to produce the same output as the average efficient

bank. This divulges that Indian public sector banks, in general, have not been successful in

employing best-practice production methods and achieving the maximum outputs from theminimum cost of inputs. Apparently, there exists substantial room for significant cost savings if

Indian PSBs use and allocate their productive inputs more efficiently.

15


16/41

Table 1: Mean cost, technical, and allocative efficiencies in Indian public sector banking industry: an aggregate analysis

Panel A: Year-wise mean efficiency scores

Bank Groups All PSBs SBI Group NB Group

Year CE TE AE CE TE AE CE TE AE

1992/93 0.710 0.773 0.917 0.928 0.946 0.981 0.617 0.700 0.890

1993/94 0.756 0.784 0.962 0.981 0.993 0.988 0.661 0.696 0.952

1994/95 0.774 0.824 0.938 0.947 1.000 0.947 0.700 0.750 0.935

1995/96 0.782 0.812 0.961 0.954 0.985 0.969 0.710 0.740 0.957

1996/97 0.793 0.819 0.967 0.964 0.985 0.978 0.721 0.749 0.963

1997/98 0.806 0.848 0.949 0.961 0.992 0.969 0.741 0.787 0.941

1998/99 0.782 0.834 0.938 0.959 0.992 0.967 0.707 0.768 0.925

1999/2000 0.772 0.827 0.936 0.940 0.974 0.964 0.701 0.765 0.9252000/01 0.774 0.819 0.947 0.928 0.945 0.981 0.709 0.766 0.932

2001/02 0.763 0.822 0.931 0.875 0.936 0.934 0.716 0.774 0.930

2002/03 0.825 0.861 0.959 0.890 0.909 0.979 0.797 0.840 0.951

2003/04 0.823 0.877 0.938 0.868 0.923 0.942 0.804 0.858 0.936

2004/05 0.839 0.880 0.956 0.891 0.947 0.941 0.818 0.851 0.962

2005/06 0.855 0.906 0.945 0.895 0.955 0.938 0.838 0.885 0.947

2006/07 0.867 0.916 0.946 0.890 0.936 0.951 0.858 0.908 0.944

2007/08 0.816 0.898 0.912 0.821 0.943 0.876 0.814 0.879 0.927

Panel B: Grand Mean of efficiency scores

Entire study period 0.796 0.844 0.944 0.918 0.960 0.956 0.745 0.795 0.939

First phase of reforms 0.772 0.814 0.948 0.957 0.985 0.971 0.694 0.741 0.938

Second phase of reforms 0.815 0.867 0.941 0.889 0.941 0.945 0.784 0.836 0.939

Panel C: Hypothesis testing: Kruskal Wallis test

Observed K-value 3.248 5.936 1.243 10.114 9.141 4.057 5.672 7.868 0.101

p-value 0.072 0.015 0.265 0.001 0.002 0.044 0.017 0.005 0.751

Inference Reject oH Reject oH Accept oH Reject oH Reject oH Reject oH Reject oH Reject oH Accept oH

Panel D: Growth Rates of mean efficiency scores

Entire study period 0.868* 0.962* -0.064 -0.845* -0.275 -0.421* 1.761* 1.655* 0.083

First phase of reforms 0.829 0.749** 0.139 -0.178 0.228 0.023 1.462** 1.294* 0.190

Second phase of reforms 0.894** 1.108* -0.203 -1.302* -0.559 -0.725* 1.967* 1.902* 0.010

Note: (i) CE, TE and AE stands for cost, technical and allocative efficiencies, respectively, (ii) The arrows and indicate that mean CE, TE and AE of the bank has increased and decreased,

respectively in the second phase of reforms relative to what has been observed during first phase of reforms.

Source: Authors calculations

16


17/41


18/41

losses in efficiency in SBI group, and this led to no significant change in cost efficiency of public

sector banking industry with the progress of deregulation process.

5.2 Sources of cost (in)efficiency

Recall that technical and allocative efficiencies are two mutually exclusive components

of cost efficiency. As a result, cost inefficiency stems from technical inefficiency (i.e., wastageof inputs in producing a certain output bundle) and/or allocative inefficiency (i.e., failing to react

optimally to relative prices of inputs). Further, technical inefficiency emanates from theinefficient functioning of the management in utilizing inputs in production process, whilst

allocative inefficiency occurs due to stringent regulatory environment inhibiting the correct mix

of inputs. Regulation is typically given as a major source of allocative inefficiency, whiletechnical inefficiency is attributed to lack of strong competitive pressures, which allow bank

managers to continue with less than optimal performance. Because it relies solely on the amounts

of inputs and outputs in its calculation and does not involve factor prices, which are mostly

market or regulation driven, technical inefficiency is entirely under the control of bankmanagement and thus results directly from management laxity and errors (Reda and Isik, 2006).

Table 1 also gives the year-wise estimates of technical and allocative efficiencies forIndian public sector banking industry and its distinct segments. From Panel A of the table, we

note that over the years understudy, the average technical efficiency is 84.4%, indicating that an

average PSB wasted about 18.5 % of factor inputs in the production process by operating off theefficient production frontier. The observed level of average allocative efficiency is 94.4%,

pointing that average PSB incurred about 5.9% more production cost by choosing the incorrect

input combination given input prices. For determining the dominant source of cost inefficiency,we make a comparison of the relative sizes of technical and allocative inefficiency levels. We

note that, for all the sample years, allocative efficiency is consistently higher than technical

efficiency, which signals that technical inefficiency (i.e., underutilization or wasting of inputs)

has greater significance than allocative inefficiency (i.e., choosing the incorrect inputcombination given input prices) as a source of cost inefficiency within all inefficient PSBs. This

result suggests that the observed cost inefficiency in Indian public sector banking industry

originates primarily due to managerial problems in using the financial resources rather thanregulatory environment in which PSBs are operating. Apparently, the managers of PSBs operate

relatively efficient with respect to the optimal combination of inputs given their prices and

technology, yet they are not efficient in transforming bank inputs into outputs. Turning to thesegment-wise analysis, we note that average cost inefficiency in NB group is primarily driven by

technical inefficiency rather than allocative inefficiency. However, in SBI group, both technical

and allocative inefficiencies are roughly equally important source of cost inefficiency.

Turning to the impact of deepening of banking reforms, it has been observed that average

technical efficiency of Indian public sector banking industry has increased by 5.3% in the secondphase of reforms than what has been observed in first phase (86.7% vis--vis 81.4%). Further,this gain in the average technical efficiency has been observed to be statistically significant as

indicated by the rejection of null hypothesis in Kruskal-Wallis test. Regarding average allocative

efficiency, we note that an increase in the intensity of reforms did not bring any significantchange in its level. The acceptance of null hypothesis in Kruskal-Wallis test confirms this. The

segment-wise analysis reveals that in the second phase of reforms, a statistically significant gain

in average technical efficiency in tune to 9.5% has noted for NB group, while a statistically

18


19/41

significant decline in average technical efficiency in order of (-)4.4% has been observed for SBI

group. The analysis further reveals that a statistically significant decline in average allocativeefficiency by (-)2.6% has taken place in SBI group. Nevertheless, mean allocative efficiency

shown a negligible increase during the second phase of reforms, which is further observed to be

statistically insignificant. Peeping deep into the results, we note that what so ever increase in cost

and technical efficiencies in public sector banking industry has taken during the study period thatwas contributed by the significant improvement in technical efficiency of nationalized banks. In

fact, the drag in allocative efficiency of the banks belong to SBI groups is not only responsible

for a decline in allocative efficiency of the public sector banking industry as whole, but alsooffset, to a great extent, the impact of gains in technical efficiency of nationalized banks on the

cost efficiency of the public sector banking industry as a whole.

5.3 Growth rates analysisTo ascertain a more concrete picture about the trends of efficiency measures, we relied on

the trend growth rates of efficiency measures for the entire study period and distinct sub-periods.

For computing the average annual growth rate of mean efficiency score for the entire study

period, we estimated the log-linear trend equation: ln t tE t = + + , where tE is mean efficiency

score in yeartand t=1,2,,Tdenotes time and t

denotes stochastic error term. Following Boyce(1986), a kinked exponential model has been used for estimating the growth rates for the sub-

periods. The regression equation in kinked exponential model for estimating the growth rates for

sub-periods takes the form: 1 2ln ( (1 ) ) (1 )( )t tE Dt D k D t k = + + + + , where D is a dummy

variable (D=1 for first sub-period and 0 for second sub-period), k is the midpoint of the two

discontinuous series (k=7.5 in the present study). The OLS estimates of 1 and 2 (i.e., 1 2 and )

gives the growth rates for the first and second sub-periods, respectively.Panel D of Table 1 provides the growth rate estimates of cost efficiency and its

components. We note that cost efficiency of Indian public sector banking industry grew at a

modest rate of 0.868% per annum over the entire study period. Further, it has declined at the rate

of (-)0.845% per annum for SBI group and recorded a decent growth rate of 1.761% per annum

for NB group. The analysis of growth rates for the distinct sub-periods reveals that (a) in SBIgroup, the declining trend of cost efficiency was more pronounced in the second phase, (b) in NB

group, cost efficiency grew at the rate of 1.967% during the second phase which is about halfpercent more than what has been noticed during the first phase, and (c) the affect of decent

growth in cost efficiency in NB group during the second phase of reforms was offset to a great

extent by a pronounced decline in the same in SBI group. This led to a very slight improvementin growth of cost efficiency of Indian public sector banking industry during the second phase of

reforms relative to the first one (0.894% vis--vis 0.829%).

Turning to the growth rates of disaggregate components of the cost efficiency, we note

that, over the entire study period, technical efficiency of Indian public sector banking industryfollowed an upward trend, while allocative efficiency followed a path of deceleration. This is

evident from the figures of growth rates at 0.96% per annum and (-)0.064% per annum fortechnical efficiency and allocative efficiency, respectively. In Indian public sector bankingindustry, the components of cost efficiency moved in opposite directions, and they are

counterbalancing in nature. The segment-wise analysis reveals that in SBI group, both

components followed a declining trend over the entire study period. However, in NB group,these components posted a positive trend. It is noteworthy here that growth in technical

efficiency was relatively more impressive than that of allocative efficiency. Further, the analysis

of growth rates for distinct sub-periods reports (i) a negative trend in both components of cost

19


20/41

efficiency in SBI group during the second phase relative to a positive trend during the first phase,

(ii) a noticeable improvement in the growth of technical efficiency of Indian public sectorbanking industry as a whole and its segment of nationalized banks during the second phase

relative to first one, and (iii) the allocative efficiency of Indian public sector banking industry has

shown a decelerating trend during the latter phase relative to the former. Overall, the analysis

manifests that in Indian public sector banking industry, the growth in technical efficiencycontributed positively to the growth of cost efficiency and the deceleration in allocative

efficiency actually drags it.

5.4 Inter-bank analysis

Table 2 provides the average cost, technical and allocative efficiencies scores for

individual PSBs over the entire study period and distinct sub-periods. The perusal of table givesthat there is heterogeneity of the level of average cost efficiency across PSBs. United Bank of

India presents the lowest level of cost efficiency (58.6%) and State Bank of Hyderabad (95.3%)

displays the highest ones (95.3%). Further, in 6 PSBs, the magnitude of average cost inefficiency

is found to be less than 10%. These banks are State Bank of Hyderabad (95.3%), State Bank ofIndia (93.9%), State Bank of Indore (94.6%), State Bank of Mysore (93.8%), State Bank of

Patiala (93.2%), and Corporation Bank (91.3%). We can rightly designate these banks as

marginally cost inefficient banks. It is significant to note here that i) out of 6 marginally costinefficient banks, 5 banks belong to SBI group, ii) all the observed marginally inefficient banks

have both high level of technical and allocative efficiencies, and ii) average technical efficiency

is more than average allocative efficiency in all these banks.In the remaining 21 PSBs, the average cost efficiency ranged between 58.6% and 89.6%,

indicating that the extent of cost inefficiency lies in the range between 11.6% and 70.6%. These

banks can be categorized as distinctively cost inefficient banks. Two points are noteworthyhere that (a) in 20 distinctively cost inefficient banks, cost inefficiency emanates primarily due to

technical inefficiency rather than allocative inefficiency, (b) the three banks viz., Indian

Bank(67.4%), UCO Bank(58.9%) and United Bank of India(58.6%) which were identified as

weak banks by the Committee on the Banking Sector Reforms (1998) and Working Group onRestructuring of Weak Public Sector Banks (1999) are the least cost efficient banks in the

sample.

20


21/41

Table 2: Cost, technical and allocative efficiencies in Indian public sector banking industry: a bank-wise analysis

Efficiency measures Cost efficiency (CE) Technical efficiency (TE) Allocative efficiency (AE)

Period of study

Bank

First phase

of reforms

Second

phase of

reforms

Entire

study

period

First

phase of

reforms

Second

phase of

reforms

Entire

study

period

First

phase of

reforms

Second

phase of

reforms

Entir

stud

perio

State Bank of Bikaner and Jaipur 0.960 0.845 0.896 0.991 0.867 0.921 0.969 0.974 0.972

State Bank of Hyderabad 0.945 0.960 0.953 0.984 0.994 0.990 0.960 0.965 0.963

State Bank of India 0.997 0.893 0.939 0.998 0.947 0.970 0.998 0.943 0.967

State Bank of Indore 0.978 0.922 0.946 0.979 0.974 0.976 0.999 0.946 0.969

State Bank of Mysore 0.931 0.944 0.938 0.982 0.993 0.989 0.948 0.950 0.949

State Bank of Patiala 0.969 0.903 0.932 0.991 0.992 0.992 0.978 0.910 0.940

State Bank of Saurashtra 0.984 0.806 0.884 0.998 0.859 0.920 0.985 0.942 0.96

State Bank of Travancore 0.889 0.836 0.859 0.953 0.902 0.924 0.933 0.929 0.93

Allahabad Bank 0.624 0.752 0.696 0.677 0.796 0.744 0.925 0.946 0.937

Andhra Bank 0.692 0.867 0.791 0.735 0.942 0.851 0.946 0.919 0.93

Bank of Baroda 0.868 0.794 0.827 0.947 0.858 0.897 0.918 0.928 0.924

Bank of India 0.683 0.751 0.721 0.741 0.792 0.770 0.924 0.949 0.93

Bank of Maharashtra 0.697 0.773 0.740 0.729 0.799 0.768 0.949 0.968 0.960

Canara Bank 0.798 0.765 0.780 0.857 0.816 0.834 0.933 0.940 0.937

Central Bank of India 0.619 0.733 0.683 0.643 0.771 0.715 0.961 0.952 0.956

Corporation Bank 0.920 0.907 0.913 0.968 0.997 0.984 0.952 0.910 0.92

Dena Bank 0.793 0.831 0.814 0.834 0.879 0.859 0.950 0.944 0.947

Indian Bank 0.570 0.754 0.674 0.665 0.781 0.730 0.869 0.963 0.922

Indian Overseas Bank 0.662 0.822 0.752 0.704 0.852 0.787 0.942 0.965 0.95

Oriental Bank of Commerce 0.897 0.833 0.861 0.965 0.986 0.977 0.927 0.845 0.88

Punjab & Sind Bank 0.579 0.783 0.694 0.613 0.830 0.735 0.946 0.947 0.947

Punjab National Bank 0.743 0.857 0.807 0.772 0.889 0.838 0.961 0.964 0.962

Syndicate Bank 0.639 0.771 0.713 0.683 0.823 0.762 0.932 0.937 0.93

UCO Bank 0.520 0.644 0.589 0.551 0.676 0.621 0.946 0.953 0.950

Union Bank of India 0.754 0.772 0.764 0.832 0.850 0.842 0.912 0.909 0.910

United Bank of India 0.444 0.696 0.586 0.461 0.722 0.608 0.959 0.966 0.963

Vijaya Bank 0.686 0.787 0.743 0.711 0.833 0.779 0.964 0.945 0.953

Note: The arrows and indicate that mean CE, TE and AE of the bank has increased and decreased, respectively in the second phase of reforms relatto what has been observed during first phase of reforms

Source:Authors calculations

The comparative analysis of average cost efficiency between the sub-periods provides the

following points: (i) the average cost efficiency has improved in 17 PSBs during the latter phase

of reforms relative to first one; (ii) of 8 PSBs that belong to SBI group, the average costefficiency in 6 banks recorded a downturn in the latter phase compared to the earlier phase; (iii)

the three weak banks (Indian Bank, UCO Bank and United Bank of India) have observed an

upturn in the average cost efficiency over the second phase of reforms compared to first phase;and (iv) out of 18 PSBs that belong to NB group, only in 4 banks namely, Bank of Baroda,

Corporation Bank, Oriental Bank of Commerce, and Canara Bank, a decline in average cost

efficiency has been observed during the second phase of reforms. The above result indicates that

though at the aggregate level of banking industry, no significant change in average costefficiency has been observed, but at the level of individual banks noticeable improvement in

average cost efficiency has been observed with the deepening of the process of banking reforms

since 1998/99. The main reason for insignificant improvement in the cost efficiency at theindustry level is that the downturn in the average cost efficiency among most of banks in SBI

group offsets the effect of an ascent in average cost efficiency in the majority of banks that

belong to NB group.

21


22/41

As far as the components of cost efficiency are concerned, we observe that average

technical efficiency has increased in the 20 PSBs during the second phase of reforms relative tofirst one. This indicates that operating efficiency of majority of PSBs improved with the increase

in the intensity of reforms. The connotation of this finding is that PSBs have learnt to avoid the

waste of inputs in transforming outputs with the deepening of reforms. Further, we note that in

15 PSBs, the average allocative efficiency has increased relatively in the latter phase of reformscompared to first one. Thus, the majority of PSBs have learnt to organize the inputs in the cost-

minimizing way given their prices. On the whole, we observed that a majority of PSBs exhibited

a decline in both technical and allocative inefficiencies with the ascent of deregulation in Indianbanking industry.

The inter-bank analysis of trend growth rates of cost efficiency and its disaggregatecomponents is provided in the Table 3. The results show that (i) the cost efficiency in majority

of banks that belong to SBI group followed a declining trend. This is evident from the fact that,

of 8 PSBs in SBI group, 6 banks posted a negative growth rate over the entire study period; (ii)

barring 4 PSBs, the remaining 15 banks belonging to NB group experienced an increasing trendin cost efficiency. The highest growth in cost efficiency has been observed in United Bank of

India (5.89%), followed by Bank of Maharashtra (4.65%) and Punjab & Sind Bank (4.10%); andiii) in 20 PSBs, cost efficiency and its disaggregate components evolve with the same tendency.That is, an increasing(decreasing) trend in cost efficiency is followed by the

increasing(decreasing) trend in technical and allocative efficiencies. This undertones the

presence of a phenomenon of co-movement in the growth of cost, technical and allocative inIndian public sector banking industry.

Turning to the analysis for the distinct sub-periods, it has been observed that the numberof banks having a positive trend in cost efficiency (technical efficiency, allocative efficiency)

during the second phase of reforms was 18(18,12), while this number stood at 17(18,19) during

the first phase. This highlights that the number of banks showing downtrend in allocative

efficiency has increased considerably during the latter phase of reforms. In a great majority ofbanks in SBI group, a declining trend in cost efficiency and its components has been noticed in

the second phase. Further, only 9(8,6) PSBs experienced an improvement in the growth rate of

cost efficiency (technical efficiency, allocative efficiency) in the second phase relative to thefirst one. This conveys that no considerable improvement in the growth of cost efficiency and its

components has been noticed in Indian public sector banking industry with the ascent in the

intensity of reforms since 1998/99. Considering both the sub-periods separately, we noticed theappearance of co-movement in trend growth rates of cost, technical and allocative efficiencies in

majority of PSBs. By and large, the results of growth rates of cost efficiency are in consonance

with the changes in average cost efficiency levels between the first and second phases of banking

reforms. The inter-bank analysis indicates that to a large extent, the Indias experience with

banking reforms offers a success story to be emulated by other developing economies, since themajority of the PSBs experienced a positive trend in cost efficiency during the reforms period.

22


23/41

Table 3: Growth rates of cost, technical and allocative efficiencies: an inter-bank analysis

Efficiency measures Cost efficiency (CE) Technical efficiency (TE) Allocative efficiency (AE)

Period of study

Bank

First

phase of

reforms

Second

phase of

reforms

Entire

Study

Period

First

phase of

reforms

Second

phase of

reforms

Entire

Study

Period

First

phase of

reforms

Second

phase of

reforms

Entir

Study

Perio

State Bank Of India -3.578 1.437 -0.533 -2.689 1.326 -0.231 -0.569 0.372 -0.0

State Bank Of Bikaner & Jaipur 1.075 -0.392 0.205 0.487 -0.137 0.116 0.589 -0.259 0.0

State Bank Of Hyderabad -0.459 -1.958 -1.348 -0.637 -0.452 -0.528 0.178 -1.512 -0.8

State Bank Of Indore 2.168 -3.259 -0.835 1.393 -1.043 -0.052 0.761 -2.722 -1.4

State Bank Of Mysore 1.506 -0.524 0.301 0.953 -0.272 0.226 0.554 -0.251 0.07

State Bank Of Patiala 1.011 -3.107 -1.645 0.021 -0.107 -0.055 0.991 -3.003 -1.6

State Bank Of Saurashtra -0.139 -4.311 -2.819 0.690 -4.386 -2.476 -0.824 0.068 -0.2

State Bank Of Travancore -2.554 1.284 -0.182 -1.062 0.436 -0.173 -1.494 0.845 -0.1

Allahabad Bank 3.986 1.197 2.331 3.090 1.611 2.213 0.882 -0.403 0.12

Andhra Bank 4.822 2.722 3.576 7.428 0.999 3.613 -2.598 1.720 0.05

Bank Of Baroda -2.643 0.807 -0.596 -3.496 1.008 -0.506 0.853 -0.201 0.22

Bank Of India 1.773 1.155 1.406 1.417 0.950 1.140 0.357 0.207 0.26

Bank Of Maharashtra 8.796 1.370 4.657 6.885 1.480 3.863 1.232 -0.221 0.37

Canara Bank -2.011 0.720 -0.390 -2.331 1.209 -0.230 0.327 -0.488 -0.1

Central Bank Of India 7.416 -0.940 2.034 7.282 -0.764 1.980 0.135 -0.175 -0.0

Corporation Bank 0.140 0.386 0.286 1.986 -0.440 0.546 -1.844 0.827 -0.2

Dena Bank 2.474 0.765 1.460 2.943 0.454 1.466 -0.474 0.306 -0.0

Indian Bank -2.121 7.774 3.795 -5.242 7.540 1.976 3.092 0.240 1.40

Indian Overseas Bank -5.421 8.078 1.596 -6.005 7.982 0.864 0.590 0.092 0.29

Oriental Bank Of Commerce 0.340 -1.097 -0.292 1.379 -0.561 0.228 -0.240 -0.866 -0.6

Punjab & Sind Bank 4.385 3.909 4.103 4.346 4.031 4.159 0.038 -0.125 -0.0

Punjab National Bank 1.711 2.666 2.277 1.381 2.625 2.119 0.327 0.050 0.16

Syndicate Bank 4.911 0.233 2.135 3.671 0.715 1.917 1.244 -0.485 0.2

UCO Bank 2.614 1.680 2.060 2.257 1.640 1.891 0.372 0.029 0.16

Union Bank Of India -1.557 2.236 1.177 -1.058 1.754 0.611 -0.495 0.486 0.0

United Bank Of India 10.688 2.607 5.893 9.848 2.938 5.748 0.822 -0.326 0.23

Vijaya Bank -0.891 -0.197 -0.498 -0.341 2.860 0.697 0.039 -1.056 -0.6

Note: The arrows and indicate that mean CE, TE and AE of the bank has increased and decreased, respectively in the second phase of reforms relativ

what has been observed during first phase of reformsSource: Authors calculations

The aforementioned empirical findings vividly indicate a positive trend in the cost

efficiency levels of Indian public sector banking industry during the post-reforms years, but

some discussion on what derived this improvement is warranted here.In this context, the mostsignificant factor is the heightened competition in Indian banking sector during the post-reforms

period due to relaxed entry norms forde novo private domestic and foreign banks. To keep their

survival intact in the highly competitive environment, the PSBs, especially the weak ones, startedallocating resources efficiently, and changed their behavioural attitude and business strategies.

Further, in their drive to achieve higher levels of operating efficiency, Indian PSBs during the

post-reforms years, primarily concentrated on the rationalization of the labour force andreduction in the cost of financial transactions. For making optimal use of labour force, these

banks evolved policies aimed at rightsizing and redeployment of the surplus staff either by

way of retraining them and giving them appropriate alternate employment or by introducing a

voluntary retirement scheme (VRS) with appropriate incentives. Consequently, the labour costper unit of earning assets fell from 2.44 % in 1992/93 to 0.95% in 2007/08. With the objectives

of cutting the cost of day-to-day banking operations in the long run and retaining their existing

23


24/41

customers and attracting new ones by providing new technology-based delivery channels (like

internet banking, mobile banking and card based funds transactions), PSBs made heavyinvestment in technology during the post-reforms years. Between September 1999 and March

2008, PSBs incurred an expenditure of Rs.15015 crore (1 crore=10 millions) on computerization

and development of communication networks (Reserve Bank of India 2006). The

computerization of branches and installation of ATMs are two major areas in which the use oftechnology is clearly visible. By end-March 2008, about 93.7 % branches of PSBs were fully

computerized, of which 67.7% branches of nationalized banks and 95% of SBI and its associates

were under core banking solutions. The number of both on-site and off-site ATMs by PSBsincreased from 3473 at the end of March 2003 to 34789 at the end of March 2008. On the whole,

the post-reforms period witnessed enhanced level of IT usage by public sector banks which

might have contributed to efficiency improvement.

Another major influential factor that contributed to cost efficiency gains is that due to

profound changes in the regulatory and legal frameworks, there has been a better recovery of

non-performing loans which led to an improvement in the assets quality of the PSBs. This isevident from the fact that in public sector banking segment, the quantum of net NPAs as

percentage of net advances declined from 10.7% in 1994/95 to 0.99% in 2005/06. Among thevarious channels of recovery available to banks for dealing with bad loans, SARFAESI Act andthe debt recovery tribunals(DRTs) have been the most effective in terms of amount

recovered(RBI, 2008). Due to better recovery of NPAs, the share of net-interest income in total

income of PSBs has increased significantly. Further, in the Indian banking industry, the off-balance sheet activities business has soared during the post-reforms years. This has led to

increase in other income of the PSBs. The improvement in efficiency could also be attributable

to the fact that there has been a change in the orientation of PSBs from social objectives towardsan ascent on profitability, particularly, given that with the dilution of the government equity in

most of these banks, a stake of private investors is involved. The capital market discipline

imposed on PSBs since 1992/93 when these banks were allowed to raise capital from stock

market has led to significant efficiency gains. From the above discussion, we may infer that costefficiency gains in Indian public sector banking during the post-reforms years stemmed not only

due to cost-curtailing measures adopted by PSBs, but also occurred due to measures aiming at

augmenting income-generating capacity of banks.

5.5 Convergence in efficiency levels

5.5.1 Testing of -convergence

The concept of convergence as used in the present study refers to the tendency for two or

more banks to become similar in terms of efficiency levels. Therefore, if the banks with lowlevels of efficiency at the beginning of the period grow more rapidly than those with high initial

level of efficiency, convergence occurs, implying that the less efficient banks are catching-up.The literature spells out two different concepts of convergence: i) -convergence; and ii) -

convergence (see Barro and Sala-i-Martin 1991, 1992, 1995; Sala-i-Martin 1996a, 1996b).

Convergence of -type considers whether gaps between inefficient and efficient banks declineover time. The concept of -convergence is said to exist if the distribution of efficiency levels

across banks gets tighter over time, thus reducing some measure of dispersion over time. It

focuses on the evolution of cross-sectional distribution of efficiency over time. The existence of -convergence implies a tendency of efficiency levels to be equal across banks over time. The

24


25/41

-convergence can be tested empirically by regressing the standard deviations (or coefficient of

variations) of the cross-sections over time on a trend variable. Symbolically, it implies that

ln( or ) (4)SD CV a t t t t = + +

whereSD

tandCV

t denote the standard deviation and coefficient of variation of efficiencymeasure across all banks, a is a constant and t is a trend variable. A negative and significantslope coefficient sigma ( ) is taken as evidence for -convergence, i.e., a decline in SD (orCV)

of efficiency measure over time implies a narrowing of the dispersion of efficiency levels.

Table 4 presents the regression results pertaining to -convergence. In all the nine

regression equations given in Column 1 of Panel A, B and C, the natural logarithm ofstandard

deviations of cost, technical and allocative efficiencies scores, respectively is taken as dependentvariable which is regressed on trend variable t.Further, the regression equations given in Column

2 involves the natural logarithm of coefficient of variations of cost, technical and allocative

efficiencies scores as dependent variable which is also regressed on trend variable t. The results

reveal that in the regression equations given in Panels A and B, the estimated parameter

(which is the coefficient of trend variable t) bearsa negative sign and is statistically significant

for the first sub-period and entire study period; whereas it is negative and insignificant in the

regression equations for the second sub-period. Further, all the regression equations show a

reasonable goodness of fit with the values of 2R greater than 70% for the first sub-period and

entire study period. From the regression equations pertaining to mean allocative efficiency, as

given in Panel C, we note that the estimated parameter is positive and insignificant for theentire study period. However, for the sub-periods, the sign of the parameter has been observed

to be negative and insignificant.

The aforementioned empirical findings highlight that dispersion in the distribution of cost

and technical efficiencies scores have decreased for the first sub-period and entire study period.

This implies that the gap between both cost and technically inefficient and efficient PSBs hasdeclined significantly during the entire study period and this phenomenon of narrowing the gap

was more pronounced in the first phase of reforms relative to second one. Further, some

insignificant signs of -convergence in allocative efficiency levels appeared in the sub-periods

but on the whole no significant convergence in allocative efficiency levels has been noted inIndian public sector banking industry during the entire period under investigation. In a nutshell,

the results confirm the presence of strong -convergence in the cost and technical efficiencies

levels in Indian public sector banking industry throughout the entire study period.

25


26/41

Table 4: Testing for convergence

Panel A: Cost efficiency

Period Regression equations

Column 1 Column 2

Panel A: Cost efficiency (CE)Entire period

(1992/93-2007/08)2

*ln( ) 1.54 0.0546

(-19.41) (-6.65) ( R =76.0%)

tSD t=

2

*ln( ) 1.24 0.0633

(-14.95) (-7.40) (R =79.6%)

CV tt =

First generation

(1992/93-1998/99)2

*ln( ) 1.49 0.0589

(-24.73) (-4.38) (R =79.3%)

SD tt =

2

*ln( ) 1.16 0.0747

(-18.64) (-5.36) (R =85.2%)

CV tt =

Second generation

(1999/2000-2007/08)2

ln( ) 2.13 0.0181

(-17.46) (-0.84) (R =9.1%)

SD tt =

2

ln( ) 1.86 0.0317

(-13.61) (-1.30) (R =19.5%)

CV tt =

Panel B: Technical efficiency (TE)

Entire period

(1992/93-2007/08)2

*ln( ) 1.54 0.0527

(-19.19) (-6.37) R =74.3%)(

SD tt

=

2

*ln( ) 1.28 0.0623

(-15.42) (-7.24) ( R =78.9%)

CV tt

=

First generation

(1992/93-1998/99)2

*ln( ) 1.48 0.0608

(-20.15) (-3.70) (R =73.2%)

SD tt =

2

*ln( ) 1.22 0.0744

(-14.72) (-4.01) (R =76.3%)

CV tt =

Second generation

(1999/2000-2007/08)2

ln( ) 2.04 0.0285

(-15.03) (-1.18) (R =16.6%)

SD tt =

2

ln( ) 1.83 0.0432

(-12.66) (-1.69) (R =28.9%)

CV tt =

Panel C: Allocative efficiency (AE)

Entire period

(1992/93-2007/08)2

ln( ) 3.11 0.0030

(-17.62) (0.16) ( R =0.2%)

SD tt = +

2

ln( ) 3.06 0.0036

(-16.61) (0.19) (R =0.3%)

CV tt = +

First generation(1992/93-1998/99)

2

ln( ) 2.91 0.0571

(-8.51) (-0.75) (R =10.0%)

SD tt =

2

ln( ) 2.84 0.0596

(-7.95) (-0.75) (R =10.0%)

CV tt =

Second generation

(1999/2000-2007/08)2

ln( ) 3.02 0.0048

(-13.68) (-0.12) (R =0.2%)

SD tt =

2

ln( ) 2.97 0.0034

(-12.95) (-0.08) (R =0.1%)

CV tt =

Note: Figures in parentheses are the tvalues of the respective coefficients. * indicates that coefficients are significantly

different from zero at 5 % level of significance.

Source: Authors calculations.

5.5.2 Testing of -convergence

5.5.2.1 Absolute -convergenceThe concept of -convergence relates to catch-up phenomenon. Convergence of -type

considers whether the improvement in efficiency exhibit a negative correlation with the initial

level of efficiency. There exists -convergence in a cross-section of banks, if the inefficient

banks tends to improve in efficiency faster than efficient ones. The existence of -convergence

can be examined empirically by estimating a cross-sectional regression of annual average growth

rates of efficiency on the initial levels of efficiency. Thus, the testing for -convergence

involves estimation of the following regression equation:

26


27/41

, , - , , - , - ,[ln( ) - ln( )] / ln( ) (5)i t t i t i t i t i t E E EE = = + +

where , , - , , -[ln( ) - ln( )] /i t t i t i t E E E = is the i-th banks average growth rate of efficiency between

the periods and -t t , respectively. is the length of the time period. If the regression coefficient

on initial level of efficiency bears a statistically significant negative sign, i.e., if


28/41

dynamics of cost efficiency in indian public sector banks

Documents